Mises Daily

A
A
Home | Library | Why Faster Is Sometimes Better, But Not Always

Why Faster Is Sometimes Better, But Not Always

  • Daily July 21 stop watch
July 21, 2014

Tags Production Theory

This article is also available as an Audio Mises Daily

Apparently all it takes to get recognition these days from the National Bureau of Economic Research is to time yourself doing math problems.

In this new paper from the NBER, S. Boragan Aruoba and Jesús Fernández-Villaverde solve the familiar stochastic neoclassical growth model using various software, programming languages, and operating systems, in search of the fastest, most efficient way to do economics. Criticisms of the model aside, we see that contemporary mainstream economics is in a holding pattern. With no new math problems, the economists have resorted to timing their computers’ efforts to solve the existing math problems. This raises some very important questions: where is the “fresh” economics, and is faster production necessarily better?

From my seat in the research wing of the Mises Institute, I’m close to some interesting and — in terms of a connection to real, human market actors — relevant work in economics. Peter St. Onge, whose article you may have read a couple weeks ago, is working on a Mengerian approach to corporate strategy in product offerings, owing heavily to subjective utility and Austrian entrepreneurship theory. Audrey Redford is hard at work on a valuable extension of Mark Thornton’s Economics of Prohibition, specifically focusing on the history of the Harrison Narcotics Tax Act of 1914, using a Misesian framework to analyze prior state interventions. Dante Bayona just presented a very promising attempt to expand Roger Garrison’s capital-based macroeconomics to open economies and international trade, and is finishing a paper on Rothbard’s Aristotelian-minded praxeology.

The list of great research, just in this wing, continues, including Kyle Marchini’s work on private product certification, Jingjing Wang’s work on the history of thought on entrepreneurship, Matei Apavaloaei’s work on political entrepreneurship and protectionism, Ludvig Levasseur’s work on Kirznerian alertness, and Arkadiusz Sieron’s work on the Cantillon effects through a business cycle. And I haven’t even mentioned the great quantity and quality of work from the faculty at the end of the hall: Professors Klein, Salerno, and Thornton, who will all be teaching this week at Mises University.

Roundaboutness, Booms, and Production Time

Although prediction of future events is inherently problematic, especially concerning human action, I can confidently say that none of the economists in this office will ever patrol the halls with a stopwatch, timing our research production. This would be preposterous because we understand the “roundaboutness” of production is causally related to the value of the product — entrepreneurs will only choose longer production processes if they estimate consumer valuations of the product are still more than the costs of production, including the cost of waiting for the process to actually produce the good(s) (this is, of course, only sustainable and growth-inducing absent artificial manipulation of interest rates). Longer production (not-so-intuitively), means better, more-highly-valued products.

Note that this does not mean that longer production for the sake of longer production is valuable (a common misconception of Böhm-Bawerk’s theory), just that longer production is only employed when the estimations of the increase in value of the product exceed the increase in the costs associated with waiting.

Artificial manipulation of interest rates throws all of this coordination of consumer and producer valuations of time, capital, and production into a mess. When longer production processes are made to look more profitable, and saving is made to look less profitable, a massive rerouting of capital occurs. Entrepreneurs, seeing the decreased opportunity cost of producing goods and services, divert capital into longer production processes, while consumers at the same time increase their expenditures on final goods and services. Given a scarce supply of capital, the capital market turns into a fierce game of Hungry Hungry Hippos, which drives up the prices for these factors and therefore the costs of production as a whole.

Boom Leads to Bust, Even in Higher Ed

Consumers have the final say in the matter, however. As the chief appropriators of funds, they assert their unchanged, real time preferences by not buying the final goods at the inflated prices — inflated because the entrepreneurs anticipated that the longer production processes (more waiting) were profitable! As soon as this unsustainable course is realized, including the realization that the initial reallocation of resources was based on incorrect and misinformed estimations of consumer demands, the market crashes: demand for the factors fall, including wages and total employment, consumers pay for the prior indulgence, and economic activity as a whole stagnates, mired in uncertainty and pessimism as capital and labor move back into valuable and sustainable lines of production.

Could this type of discoordination reach higher education and research institutions? Could the supercomputer designed to simulate the entire planet that’s sitting in some economics professor’s office be a malinvestment? Probably. The economics researcher with the utmost concern for a computer with all the bells and whistles reminds me of the new, less-than-creditworthy homeowner, with the utmost concern for a house with all the bells and whistles and the square footage of a medieval castle.

Aruoba and Fernandez-Villaverde’s paper is innocent enough — they’re just trying to introduce young graduate students to different computer programs and encourage proficiency in various programming languages. But their paper is as telling as it is innocent. Perhaps the academic economics community should stress realism over RAM, conceptual rigor over computer speed, and respect for the complex, dynamic intricacies of human action over complex math problems.

Image source: iStockphoto


Note: The views expressed on Mises.org are not necessarily those of the Mises Institute.

Follow Mises Institute